Measurement data of a body of a patient, i.e. of an animal, in particular a human, preferably a living being, are accumulated by medical imaging systems such as (amongst others) computer tomographs (CTs), magnetic resonance tomographs (MRs), common X-ray devices, ultrasound imaging systems or the like. It can thus be concluded that such measurement data are all acquired by automatic machines which are controlled by experts but which acquire the measurement data based on a technical logic of their own. Accordingly, the measurement data themselves have an inherent logic in themselves, concerning both their data structure and the form of reports in which they are presented.
The interpretation of such measurement data, i.e. image data, is then performed by a radiologist and possibly by other experts such as the medical practitioner who decides on the diagnosis and/or treatment of the patient. Such interpretation process often implies that persons involved in it have to deal not only with the measurement data themselves but also with a pool of other information: for instance, additional measurement data (e.g. from previous measurements and/or from other medical imaging systems) can play a role as well as information of a more general kind: amongst these there count texts, be they in written form or provided as dictaphone texts.
In today's hospital practice, separate databases exist for texts (i.e. information data) and images (measurement data) as well as for other media. The databases come from different suppliers and thus have minimum interaction possibilities and facilities. Some hospital information system try to link such measurement and information data, however with limited success so far, because there is a lack of integration facilities and intelligent search capabilities over several different databases and media, i.e. measurement and information data.
Thus, also the annotation ontologies used to semantically describe and systemize both measurement data and information data are designed for such very specific media. The result is a separated system which cannot properly interact or be linked with other such systems:
For instance, image annotation ontology has been developed by the AIM project. This is described for instance in “Channin, David S. et al.: The caBIG Annotation and Image Markup Project. Journal of Digital Imaging 23, No. 2 April 2010, pp. 217-225”, the entire contents of which are hereby incorporated herein by reference. This annotation ontology is purely based on manual input by a user so far and exclusively used for the annotation of image data. Other ontologies include the Foundational Model of Anatomy (FMA) and RadLex both of which are well-known ontologies to the expert. On the other hand, the RIS (“Radiological Information System”—a text database) stores reports and text in an unstructured manner. There are no annotations or linking possibilities given so far.